Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
The presence of defects in crystalline solids affects material properties, the precise knowledge of defect characteristics being highly desirable. Here the authors demonstrate a machine-learning outlier detection method based on distortion score as an effective tool for modelling defects in crystall...
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| Auteurs principaux: | , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2020
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/9c4d246c54c44202a50b99c9a8ea072c |
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| Résumé: | The presence of defects in crystalline solids affects material properties, the precise knowledge of defect characteristics being highly desirable. Here the authors demonstrate a machine-learning outlier detection method based on distortion score as an effective tool for modelling defects in crystalline solids. |
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